When carrying out conventional data backup approaches it is difficult to efficiently identify fine-granularity changes—e.g., data block-level changes—that have occurred to files/directories (also referred to herein as “file nodes”) between a current state of the file system volume and a previous state of a file system volume (i.e., when the file system volume was last backed up). Consequently, conventional approaches often involve (1) identifying file nodes that have changed in any manner since the last backup, and (2) providing complete copies of the file nodes to the server. In many cases, executing a backup based on file-level changes can be extremely wasteful of resources considering that relatively small changes can be made to large file nodes. For example, an introduction of subtitles to a high-definition video will increase its size by a relatively small amount, yet the entire high-definition video will be captured and provided in the backup simply due to the fact that a file-level change has occurred.
Notably, the increasing trend in hardware complexity and overall richness of file content is leading only to larger average file sizes, and therefore is exacerbating the overall inefficiency of backups that are performed at a file-level granularity. In particular, higher levels of energy and bandwidth are required both at the computing device and the destination storage system to process backups that are performed at a file-level granularity, and, as mentioned above, this will only worsen as the average size of files increases over time. Accordingly, there exists a need for a more efficient technique for periodically backing up data of a computing device.